Will AI make humans smarter or dumber? Can our leaders impact the answers?
- Natasha Sodhi
- 17 hours ago
- 6 min read
The AI smarts dilemma
“The development of full AI could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”
- Stephen Hawking
You may be tempted to answer “smarter”, before we even get started. No wonder why. Everyday, we hear of the remarkable AI advancements globally, with the much anticipated acceleration to AGI and ASI over the next coming years. But what does this AI advancement curve, and the corporate adoption of it, truly mean for our society, and for its entanglement with the evolution of human intelligence and potential? In some ways, we are entering the tidal wave of next industrial revolution, with similar ethical challenges and philosophical questions about the future of the workforce .
• While AI will indeed make corporate tasks exceedingly effortless, and increase economic workforce efficiency, will AI truly make the average human smarter or dumber?
• At the end of the day, humans will still be our winning competitive edge in the age of AI as the curve normalizes, so are we keeping our eyes on the prize?
• Can we influence what is coming for human evolution, if we plan the AI expansion in a more concerted and curated way?
The so what, is embracing the implications of letting our future human race proceed on the dumbing down route while AI proceeds on the smarting up route. Will the two curves cross each other soon to a point of no return, leading us to the science fiction prophecies?
Leadership Gaps in Strategic AI vs Human Capital Planning
“We want to build intelligence that augments human abilities and experiences.” - Satya Nadella
Leading corporations, and consulting firms have been in an unprecedented competition over the last few years, on who can flip the script with AI, and be one of the first sector winners to truly win big. Similar to the preceding Digital transformations, AI will start bringing disproportionate returns to the top 20% players, at an accelerated pace, in a span of 2-3 years. Those who were not the true firsts, haven’t yet lost, and need to leverage both top line product and people innovation, instead of repetition of AI efficiency plays.
I led development of the flagship AI strategy offering and adoption tactics for a global consulting leader. As the AI hype caught on, it became obvious that the old school corporate consulting world was rapidly eroding in value, and market shares were doomed to diminish without a true strategic “play to win” pivot.
As AI products/offerings, talent hunts and earnings mentions intensified, AI has become an increasingly prominent priority to shape organizational outcomes. Several trends started emerging on the AI vs human capital front:
Lack of strategy for human value delivery elevation as AI creates efficiency paradigm - With efficiency as a core AI outcome, AI will address both RPA type “dumber” tasks, as well as increased LLM type “intelligence” tasks. However, there is a fundamental gap in consciously filling this newly emptied productivity time capsule with in-depth human thinking as the real value to deliver deeper outcomes. The “play to win” cannot be purely efficiency and price advantage, a true value component is required as a core differentiator.
Missing employee alignment on strategic AI value contribution to the corporate vision - Majority of employees do not have a clear understanding of why AI matters to your organization’s 5-10 year vision, what the AI “possibility” truly represents beyond the LLM models, and how they can use AI to innovate beyond mere efficiency paradigm. Lack of this understanding limits your organization’s edge to scale your critical human power to truly innovate with AI
Unclear leadership planning of human talent career growth paths in tandem with AI - Leaders need to now more than ever look at the big picture to intentionally plan how they will grow their people in the light of the “AI revolution” to achieve their most optimal potential across their career ladders, both for the experienced players, and for the newbie hires. Most old school apprenticeship/mentorship models may no longer work in the traditional sense, especially since the currency of what will matter for “talent” is evolving more rapidly than what the traditional HR departments may be planning around.
Limited thoughtful benchmarks for “best in class” AI training foundation - Leaders are mostly training their employees on common-sense AI learning based on most parroted “ChatGPT” prompting and querying to demonstrate LLM use cases in the context of increasing organizational efficiency. The training is not always rooted in strong foundation of what AI, ML and data even mean as a starting point for your sector, before delving into the application side, with a lens into the future progression. Strong foundations can lead to accelerated outcomes. Per a McKinsey survey, 48% employees believe that training is most important for GenAI adoption, even if its not advanced.
How can we tilt the human vs AI future scales?
In order for AI to truly prop up the human capital evolution, the two need to cohesively shape their symbiosis to flow together. We cannot expect every human to individually define their strategic entanglement journey and final destination with AI.
Active monitoring of AI impact on human intelligence and consciousness:
Measurement is imperative to ensure that the most critical question of our era does not get lost in a “what if” regret for our future generations. As Stephen Hawking1 rightfully expressed his concern on AI intelligence far exceeding the human counterpart, it is important for us to come up with mechanisms for active measurement, monitoring and any required counter measures related to the progression of overall human intelligence and consciousness
Increased AI tech and sector leadership cohesion: Leadership now needs to play a bigger than ever role, with a true visionary hat. Both the tech giant leaders building out the AI technologies, as well as the corporate sector leaders using these, can benefit from being concerted with in tandem horizontal planning, as well as co-envisioning of the future, vs waiting for one to precede the other.
Intentional leadership inspiration for alignment: Leadership needs to be increasingly strategic and targeted at landing the AI message to both inspire and evolve their organization teams. When a Titanic scale ship is sailing, only the captain and core crew may know the intricacies of what they are sailing through. However, if the passengers know the next destination, they can plan what to expect and what to dress for, at the upcoming harbor. Collective employee inspiration in the right direction, builds truly competitive momentum to deliver on the vision.
Knowing and growing the true winners in the race for AI human capital: Four tiers are evolving within the modern workforce, as AI learning accelerates - the “performers”, “real adopters”, “old schoolers” and “entrepreneurial innovators”. While this is a Venn set with overlaps, each category exists in its purity as well. The more leaders can anchor the organization human capital to the enthusiastic “real adopters”, the more successful we can be in the long term, by sowing and growing the right seeds all along. Pure performers for sake of ticking the boxes, can be a good topping, but not the cream that makes the AI cake delicious.
Please leave your comment and thoughts, on how you see the AI vs human battle for your organization. This will spark exciting discussions for us to stay connected as an AI community!
About the Author
Natasha Sodhi is Chief Advisor at IvyAgents.AI. She served as a Director at PwC, leading Digital Strategy & Transformation offering, as well as the flagship AI Strategy offering. Prior to that, she developed AI and people thought leadership, while leading Digital and Data engagements, at BCG in US. Previously, she led a $60B digital product P&L for a financial services leader, actively employing AL, ML and data advantages to win in the market. She started her journey as a tech entrepreneur, leading development of innovative wireless communications products centered around data and signal analysis. She was also awarded research and entrepreneurship grants from European Space Agency and Indian Department of Scientific Research.
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